



import os
import time

import numpy as np
import onnxruntime as ort


__magic_name__  	=    "1"
__magic_name__  	=    "0"
__magic_name__  	=    "1"

__magic_name__  	=    ort.SessionOptions()
__magic_name__  	=    ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
__magic_name__  	=    ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
__magic_name__  	=    ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
__magic_name__  	=    ort.RunOptions()

__magic_name__  	=    128
__magic_name__  	=    1
__magic_name__  	=    np.ones((batch, sequence), dtype=np.intaa)
__magic_name__  	=    np.ones((batch, sequence), dtype=np.intaa)
__magic_name__  	=    np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
__magic_name__  	=    time.time()
__magic_name__  	=    2000
__magic_name__  	=    {}
for iter in range(max_iters):
    __magic_name__  	=    sess.run(
        None,
        {
            sess.get_inputs()[0].name: input_ids,
            sess.get_inputs()[1].name: attention_mask,
            sess.get_inputs()[2].name: token_type_ids,
        },
        run_options=run_opt,
    )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))






"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}



class 			lowercase					( lowercase__		):
			lowercase        =     '''openai-gpt'''
			lowercase        =     {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}






			def __init__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	str=40_478      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=512      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=768      ,SCREAMING_SNAKE_CASE_  :	Dict=12      ,SCREAMING_SNAKE_CASE_  :	Dict=12      ,SCREAMING_SNAKE_CASE_  :	int="gelu"      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.1      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=1e-5      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.02      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]="cls_index"      ,SCREAMING_SNAKE_CASE_  :	str=True      ,SCREAMING_SNAKE_CASE_  :	Any=None      ,SCREAMING_SNAKE_CASE_  :	List[Any]=True      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.1      ,**SCREAMING_SNAKE_CASE_  :	int      ,)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  vocab_size
				lowerCAmelCase												=  n_positions
				lowerCAmelCase												=  n_embd
				lowerCAmelCase												=  n_layer
				lowerCAmelCase												=  n_head
				lowerCAmelCase												=  afn
				lowerCAmelCase												=  resid_pdrop
				lowerCAmelCase												=  embd_pdrop
				lowerCAmelCase												=  attn_pdrop
				lowerCAmelCase												=  layer_norm_epsilon
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  summary_type
				lowerCAmelCase												=  summary_use_proj
				lowerCAmelCase												=  summary_activation
				lowerCAmelCase												=  summary_first_dropout
				lowerCAmelCase												=  summary_proj_to_labels
				super().__init__(**SCREAMING_SNAKE_CASE_			)


